机器人用手势主动感知人类抓握状态，提升物品交接可靠性。
Active Contact Sensing for Robust Robot-to-Human Object Handover
- 通过施加特定动作并感知反作用力，判断是否为牢固抓握。
- 在30种物体上实验，成功率97.5%，远超基线方法。
- 适合需要精准交互的场景，如医疗协助或家庭服务。
机器人向人类传递物品是辅助机器人的重要能力，从家中递饮料到手术室传递器械均需可靠完成。我们期望机器人仅在确认人类牢固抓握后才释放物品，而非误判偶然触碰。现有被动感知方法因缺乏有效扰动，难以区分不同接触状态（如牢固抓握与偶然触碰），泛化能力差。本文提出一种主动感知方法：机器人执行信息采集动作，通过感知人类施加的反作用力来推断接触状态——牢固抓握会产生多方向力，而偶然触碰则不会。我们采用贝叶斯线性模型，对机器人运动到人类受力的分段线性映射进行建模，实现抓握状态识别与主动信息获取。在12名参与者和30种刚性物体上的实验表明，该方法成功率达97.5%，比两种常见基线方法高出30%以上。
原文摘要 · Abstract (English)
Robot-to-human object handover is an essential skill for robot assistants, from serving drinks at home to passing surgical tools in the operating room. We expect robots to perform handover robustly -- to release the object only after a firm human grasp while ignoring incidental touches. Existing passive-sensing methods struggle to generalize across diverse objects and human behaviors, as they lack informative perturbations to disambiguate different contact conditions, such as firm grasp versus incidental touch. We propose an active sensing approach for robust handovers: the robot applies information-gathering motions and senses the resulting human-applied forces to infer the contact state. A firm grasp produces forces in multiple directions, while an accidental touch does not. To capture this distinction, we model the contact state with a Bayesian linear model: a distribution over piecewise-linear mappings from robot motions to human-applied forces. This model enables firm grasp detection and active information gathering. In experiments with 12 participants and 30 diverse rigid objects, our method achieved a 97.5% success rate -- over 30% higher than two common baselines.
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